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Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System

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Statistical Language and Speech Processing (SLSP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8791))

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Abstract

In this paper corpus-based information extraction and opinion mining method is proposed. Our domain is restaurant reviews, and our information extraction and opinion mining module is a part of a Russian knowledge-based recommendation system.

Our method is based on thorough corpus analysis and automatic selection of machine learning models and feature sets. We also pay special attention to the verification of statistical significance.

According to the results of the research, Naive Bayes models perform well at classifying sentiment with respect to a restaurant aspect, while Logistic Regression is good at deciding on the relevance of a user’s review.

The approach proposed can be used in similar domains, for example, hotel reviews, with data represented by colloquial non-structured texts (in contrast with the domain of technical products, books, etc.) and for other languages with rich morphology and free word order.

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Notes

  1. 1.

    http://scikit-learn.org

  2. 2.

    It should be noted that the classifier (“model + feature set” combination) with the highest rank does not necessarily demonstrate the highest average weighted F1 score. The classes 0, 1, 2 and 3 assigned to the classifiers in this paper are based on their ranks (according to non-parametric Holm-Bonferroni test) and not F1 scores.

  3. 3.

    Baseline features set is considered the simplest one, while Extended_All – the most complex one. MNB and NB models are considered the simplest models, Perceptron – a more complex one, and LogReg and linear SVM – the most complex ones (in fact, they are both similar to Perceptron but their training is more computationally expensive [5]). MNB and NB classifiers are considered similar in the degree of “simplicity” as well as LogReg and linear SVM. A simple model with complex features is considered simpler than a complex model with simple (e.g., baseline) features.

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Acknowledgement

The authors acknowledge Saint-Petersburg State University for a research grant 30.38.305.2014.

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Authors and Affiliations

  1. Saint-Petersburg State University, 7/9 Universitetskaya Nab., Saint-Petersburg, Russia

    Ekaterina Pronoza, Elena Yagunova & Svetlana Volskaya

Authors
  1. Ekaterina Pronoza
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  2. Elena Yagunova
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  3. Svetlana Volskaya
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Corresponding authors

Correspondence to Ekaterina Pronoza or Elena Yagunova .

Editor information

Editors and Affiliations

  1. University Joseph Fourier, Grenoble, France

    Laurent Besacier

  2. Rovira i Virgili University, Tarragona, Spain

    Adrian-Horia Dediu

  3. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

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Pronoza, E., Yagunova, E., Volskaya, S. (2014). Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System. In: Besacier, L., Dediu, AH., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2014. Lecture Notes in Computer Science(), vol 8791. Springer, Cham. https://doi.org/10.1007/978-3-319-11397-5_21

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  • DOI: https://doi.org/10.1007/978-3-319-11397-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11396-8

  • Online ISBN: 978-3-319-11397-5

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